Indirect Causes in Dynamic Bayesian Networks Revisited
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 703-708.
Abstract
Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a causal design, i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks.
@inproceedings{ijcai2015p105,
title = {Indirect Causes in Dynamic Bayesian Networks Revisited},
author = {Motzek, Alexander and Möller, Ralf},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {703--708},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/105},
}
TY - CPAPER TI - Indirect Causes in Dynamic Bayesian Networks Revisited AU - Motzek, Alexander AU - Möller, Ralf T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 703 EP - 708 UR - https://ijcai.org/proceedings/2015/105 ER -Copied
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